Racial/Ethnic Differences in Postmenopausal Endogenous Hormones: The Multiethnic Cohort Study
Bibliographic record
Abstract
Postmenopausal women with increased estrogens and lowered sex hormone-binding globulin (SHBG) concentrations are at increased risk of breast cancer. In the Multiethnic Cohort Study, the highest incidence rates of postmenopausal breast cancer were observed among Native Hawaiians followed by Japanese Americans, Whites, African Americans, and Latinas. Ethnic differences in endogenous sex hormone profiles may contribute to some of the variation in breast cancer incidence. Plasma concentrations of androstenedione, testosterone, estrone (E(1)), estradiol (E(2)), and SHBG were measured in 739 postmenopausal women from the Multiethnic Cohort Study (240 African Americans, 81 Native Hawaiians, 96 Japanese Americans, 231 Latinas, and 91 Whites). After adjusting for age, known breast cancer risk factors and lifestyle factors, the mean levels of testosterone, estrogen, and SHBG varied across populations (Ps < or = 0.004). Across racial/ethnic groups, Native Hawaiians had the highest mean levels of androstenedione, testosterone, and estrogens and the lowest mean levels of SHBG. Compared with Whites, Native Hawaiians had higher androstenedione (+22%, P = 0.017), total testosterone (+26%, P = 0.013), bioavailable testosterone (+33%, P = 0.002), E(1) (> or =21%; P = 0.009), total E(2) (+26%, P = 0.001), bioavailable E(2) (+31%, P < 0.001), and lower SHBG (-12% P = 0.07) levels. Compared with Whites, Japanese Americans had higher E(2) (+15%, P = 0.036) and bioavailable E(2) (+18%, P = 0.024) levels. African Americans also had higher E(1) (+21%, P = 0.004), E(2) (+20%, P = 0.007), and bioavailable E(2) (+20%, P = 0.015) levels compared with Whites, whereas mean levels in Latinas were similar to those of Whites. Many of the differences in endogenous postmenopausal hormonal milieu across these five racial/ethnic groups are consistent with the known differences in breast cancer incidence across these populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".